[Feat] support basic pcp&dcp for qwen3next (#6091)

### What this PR does / why we need it?
This PR implements Context Parallelism (CP) support for the Qwen3-Next
model, including PCP (Parallel Context Parallelism) and DCP
(Dynamic/Data Context Parallelism).

- vLLM version: v0.15.0
- vLLM main:
f176443446

---------

Signed-off-by: SunnyLee219 <3294305115@qq.com>
Signed-off-by: Jingchun Gao <gaojingchun1@huawei.com>
Signed-off-by: 白永斌 <baiyongbin3@h-partners.com>
Signed-off-by: Bai Yongbin <845473182@qq.com>
Co-authored-by: SunnyLee219 <3294305115@qq.com>
Co-authored-by: Jingchun Gao <gaojingchun1@huawei.com>
Co-authored-by: 白永斌 <baiyongbin3@h-partners.com>
Co-authored-by: Mengqing Cao <cmq0113@163.com>

tests/e2e/multicard/4-cards/long_sequence/test_basic.py

@@ -44,16 +44,15 @@ def test_models_pcp_dcp_basic():
         runner.model.generate(prompts, sampling_params)
 
     model = "vllm-ascend/Qwen3-30B-A3B-W8A8"
-    with VllmRunner(
-            model,
-            enforce_eager=True,
-            max_model_len=1024,
-            tensor_parallel_size=2,
-            prefill_context_parallel_size=2,
-            decode_context_parallel_size=1,
-            enable_expert_parallel=True,
-            block_size=128,
-            quantization="ascend",
+    with VllmRunner(model,
+                    enforce_eager=True,
+                    max_model_len=1024,
+                    tensor_parallel_size=2,
+                    prefill_context_parallel_size=2,
+                    decode_context_parallel_size=1,
+                    enable_expert_parallel=True,
+                    block_size=128,
+                    quantization="ascend",
     ) as runner:
         runner.model.generate(prompts, sampling_params)
     
@@ -71,6 +70,19 @@ def test_models_pcp_dcp_basic():
     ) as runner:
         runner.model.generate(prompts, sampling_params)
 
+    model = "Qwen/Qwen3-Next-80B-A3B-Instruct"
+    with VllmRunner(model,
+                    enforce_eager=True,
+                    max_model_len=1024,
+                    tensor_parallel_size=2,
+                    prefill_context_parallel_size=2,
+                    decode_context_parallel_size=1,
+                    max_num_batched_tokens=1024,
+                    enable_expert_parallel=True,
+                    gpu_memory_utilization=0.8,
+                    block_size=128) as runner:
+        runner.model.generate(prompts, sampling_params)
+
 
 def test_models_pcp_dcp_full_graph():
     prompts = [

tests/ut/attention/test_attention_cp.py

@@ -81,6 +81,9 @@ class TestAscendAttentionCPImpl(TestBase):
             [0])
         attn_metadata.prefill.pcp_metadata.kv_with_q_tail_mask_idx = torch.tensor(
             [0])
+        attn_metadata.prefill.pcp_metadata.pcp_fa_query_idx = torch.tensor(
+            [0, 1])
+        attn_metadata.prefill.pcp_metadata.pcp_use_hybrid_attn = False
 
         output, attn_lse = self.impl._forward_prefill_cp(
             query, key, value, attn_metadata)
@@ -257,12 +260,23 @@ class TestAscendAttentionCPImpl(TestBase):
         attn_metadata.prefill = MagicMock()
         attn_metadata.prefill.pcp_metadata.pcp_allgather_restore_idx = torch.tensor(
             [0, 3, 1, 2, 0, 0, 0, 0])
+        attn_metadata.prefill.pcp_metadata.pcp_use_hybrid_attn = False
+        attn_metadata.prefill.pcp_metadata.pcp_padded_tokens_fla = 0
+        attn_metadata.prefill.pcp_metadata.pcp_enter_fa_restore_idx = torch.arange(
+            num_tokens * 3 * self.impl.pcp_size
+        )
+        attn_metadata.prefill.pcp_metadata.pcp_unpad_mask = torch.tensor(
+            [True, False, True, True, True, True, True, True]
+        )
 
+        query = torch.rand(num_tokens, num_heads, head_size)
         key = torch.randn(num_tokens, num_heads, head_size)
         value = torch.randn(num_tokens, num_heads, head_size)
+        output = torch.rand(num_tokens, num_heads * head_size)
 
-        key, value = self.impl.reshape_and_cache(key, value, kv_cache,
-                                                 attn_metadata)
+        query, key, value, output = self.impl.reshape_and_cache(
+            query, key, value, kv_cache, attn_metadata, output
+        )
         self.assertEqual(key.shape[0], num_tokens * self.impl.pcp_size)
         self.assertEqual(key.shape[1], num_heads)
         self.assertEqual(key.shape[2], head_size)

vllm_ascend/ascend_forward_context.py

@@ -43,6 +43,7 @@ def set_ascend_forward_context(
     model_instance: torch.nn.Module = None,
     is_draft_model=False,
     skip_compiled: bool = False,
+    max_tokens_across_pcp: int = 0,
     draft_attn_metadatas=None,
 ):
     """A context manager that stores the current forward context,
@@ -139,6 +140,7 @@ def set_ascend_forward_context(
             max_tokens_across_dp = num_tokens
 
         forward_context.max_tokens_across_dp = max_tokens_across_dp
+        forward_context.max_tokens_across_pcp = max_tokens_across_pcp
 
         if num_tokens is not None:
             if num_actual_tokens is None:

vllm_ascend/attention/attention_v1.py

@@ -892,10 +892,12 @@ class AscendAttentionBackendImpl(AttentionImpl):
 
     def reshape_and_cache(
         self,
+        query: torch.Tensor,
         key: torch.Tensor,
         value: torch.Tensor,
         kv_cache: tuple[torch.Tensor],
         attn_metadata: AscendMetadata,
+        output: torch.Tensor,
     ):
         if len(kv_cache) > 1:
             if self.is_kv_producer:
@@ -915,7 +917,7 @@ class AscendAttentionBackendImpl(AttentionImpl):
             )
             if self.is_kv_producer:
                 attn_metadata.reshape_cache_event.record()
-        return key, value
+        return query, key, value, output
 
     def forward_impl(
         self,
@@ -970,12 +972,20 @@ class AscendAttentionBackendImpl(AttentionImpl):
         num_tokens = query.shape[0]
         if attn_metadata is None:
             return output.fill_(0)
+        output_padded = None
         if key is not None and value is not None:
-            key, value = self.reshape_and_cache(key, value, kv_cache, attn_metadata)
+            output_padded = output
+            query, key, value, output_padded = self.reshape_and_cache(
+                query, key, value, kv_cache, attn_metadata, output
+            )
         # pooling model branch
         if attn_metadata.model_runner_type == "pooling":
             attn_output = self._forward_encoder_attention(query, key, value, attn_metadata, output)
             output[:num_tokens] = attn_output[:num_tokens]
             return output
-        output = self.forward_impl(query, key, value, kv_cache, attn_metadata, output)
+        if output_padded is not None:
+            attn_output = self.forward_impl(query, key, value, kv_cache, attn_metadata, output_padded)
+        else:
+            attn_output = self.forward_impl(query, key, value, kv_cache, attn_metadata, output)
+        output[:num_tokens] = attn_output[:num_tokens]
         return output

vllm_ascend/attention/context_parallel/attention_cp.py

@@ -20,6 +20,7 @@ from typing import ClassVar
 import numpy as np
 import torch
 import torch.distributed as dist
+import torch.nn.functional as F
 import torch_npu
 from vllm.config import VllmConfig
 from vllm.distributed import (
@@ -209,7 +210,12 @@ class AscendAttentionCPMetadataBuilder(AscendAttentionMetadataBuilder):
                 head_attn_nomask_seqlens=head_attn_nomask_seqlens,
                 tail_attn_nomask_seqlens=tail_attn_nomask_seqlens,
                 q_full_idx=common_long_seq_metadata.q_full_idx,
+                pcp_use_hybrid_attn=common_long_seq_metadata.pcp_use_hybrid_attn,
+                pcp_unpad_mask=common_long_seq_metadata.pcp_unpad_mask,
                 pcp_allgather_restore_idx=common_long_seq_metadata.pcp_allgather_restore_idx,
+                pcp_fa_query_idx=common_long_seq_metadata.pcp_fa_query_idx,
+                pcp_padded_tokens_fla=common_long_seq_metadata.pcp_padded_tokens_fla,
+                pcp_enter_fa_restore_idx=common_long_seq_metadata.pcp_enter_fa_restore_idx,
             )
 
             prefill_metadata = AscendMetadataForPrefill(
@@ -469,6 +475,10 @@ class AscendAttentionCPImpl(AscendAttentionBackendImpl):
         kv_with_q_head_mask_idx = attn_metadata.prefill.pcp_metadata.kv_with_q_head_mask_idx
         kv_with_q_tail_nomask_idx = attn_metadata.prefill.pcp_metadata.kv_with_q_tail_nomask_idx
         kv_with_q_tail_mask_idx = attn_metadata.prefill.pcp_metadata.kv_with_q_tail_mask_idx
+        if attn_metadata.prefill.pcp_metadata.pcp_use_hybrid_attn:
+            fa_query_idx = attn_metadata.prefill.pcp_metadata.pcp_fa_query_idx
+            query = torch.index_select(query, 0, fa_query_idx)
+
         q_head = torch.index_select(query, 0, q_head_idx)
         q_tail = torch.index_select(query, 0, q_tail_idx)
         k_head_nomask = torch.index_select(key, 0, kv_with_q_head_nomask_idx) if self.pcp_rank > 0 else None
@@ -735,14 +745,18 @@ class AscendAttentionCPImpl(AscendAttentionBackendImpl):
 
     def reshape_and_cache(
         self,
+        query: torch.Tensor,
         key: torch.Tensor,
         value: torch.Tensor,
         kv_cache: tuple[torch.Tensor],
         attn_metadata: AscendMetadata,
+        output: torch.Tensor,
     ):
+        num_tokens = query.shape[0]
         num_decode_tokens = attn_metadata.num_decode_tokens
         has_decode = attn_metadata.num_decodes > 0
         has_prefill = attn_metadata.num_prefills > 0
+        output_padded = output
 
         if len(kv_cache) > 1:
             if self.is_kv_producer:
@@ -762,14 +776,23 @@ class AscendAttentionCPImpl(AscendAttentionBackendImpl):
 
             if has_prefill:
                 if self.pcp_size > 1:
-                    kv = torch.cat([key, value], dim=-1)
-                    num_actual_tokens_pcp_padded = attn_metadata.num_actual_tokens_pcp_padded // self.pcp_size
-                    all_kv = get_pcp_group().all_gather(kv[:num_actual_tokens_pcp_padded].contiguous(), dim=0)
-                    assert attn_metadata.prefill is not None
-                    assert attn_metadata.prefill.pcp_metadata is not None
-                    pcp_allgather_restore_idx = attn_metadata.prefill.pcp_metadata.pcp_allgather_restore_idx
-                    all_kv = torch.index_select(all_kv, 0, pcp_allgather_restore_idx)
-                    key, value = all_kv.split([self.head_size, self.head_size], dim=-1)
+                    assert attn_metadata.prefill is not None and attn_metadata.prefill.pcp_metadata is not None
+                    if not attn_metadata.prefill.pcp_metadata.pcp_use_hybrid_attn:
+                        kv = torch.cat([key, value], dim=-1)
+                        num_actual_tokens_pcp_padded = attn_metadata.num_actual_tokens_pcp_padded // self.pcp_size
+                        all_kv = get_pcp_group().all_gather(kv[:num_actual_tokens_pcp_padded].contiguous(), dim=0)
+                        pcp_allgather_restore_idx = attn_metadata.prefill.pcp_metadata.pcp_allgather_restore_idx
+                        all_kv = torch.index_select(all_kv, 0, pcp_allgather_restore_idx)
+                        key, value = all_kv.split([self.head_size, self.head_size], dim=-1)
+                    else:
+                        query, key, value = self._gather_and_restore_pcp_qkv(query, key, value, attn_metadata)
+                        num_actual_tokens_pcp_padded = attn_metadata.num_actual_tokens_pcp_padded
+                        output_local_padded_tokens_fa = num_actual_tokens_pcp_padded // self.pcp_size - num_tokens
+                        if output_local_padded_tokens_fa > 0:
+                            output_padded = F.pad(
+                                output, pad=(0, 0, 0, 0, 0, output_local_padded_tokens_fa), mode="constant", value=0
+                            )
+
                 prefill_key = key[self.pcp_size * num_decode_tokens : attn_metadata.num_actual_tokens_pcp_padded]
                 prefill_value = value[self.pcp_size * num_decode_tokens : attn_metadata.num_actual_tokens_pcp_padded]
                 slot_mapping = attn_metadata.slot_mapping[
@@ -784,7 +807,62 @@ class AscendAttentionCPImpl(AscendAttentionBackendImpl):
                 )
             if self.is_kv_producer:
                 attn_metadata.reshape_cache_event.record()
-        return key, value
+        return query, key, value, output_padded
+
+    def _gather_and_restore_pcp_qkv(
+        self,
+        query: torch.Tensor,
+        key: torch.Tensor,
+        value: torch.Tensor,
+        attn_metadata: AscendMetadata,
+    ):
+        """
+        Gathers QKV chunks from all GPUs in the PCP group and restores the original
+        sequence order for Context Parallelism (CP).
+        """
+        num_tokens = query.shape[0]
+        num_actual_tokens_pcp_padded = attn_metadata.num_actual_tokens_pcp_padded
+        assert attn_metadata.prefill is not None and attn_metadata.prefill.pcp_metadata is not None
+        pcp_padded_tokens_fla = attn_metadata.prefill.pcp_metadata.pcp_padded_tokens_fla
+        num_tokens_pcp_padded_fla = num_tokens + pcp_padded_tokens_fla
+
+        qkv_fla = torch.cat(
+            [query.reshape(num_tokens, -1), key.reshape(num_tokens, -1), value.reshape(num_tokens, -1)],
+            dim=-1,
+        )
+        if pcp_padded_tokens_fla > 0:
+            qkv_fla = F.pad(qkv_fla, pad=(0, 0, 0, pcp_padded_tokens_fla), mode="constant", value=0)
+        all_qkv = get_pcp_group().all_gather(qkv_fla[:num_tokens_pcp_padded_fla].contiguous(), dim=0)
+
+        # Restore the original sequence order using pre-computed indices
+        pcp_enter_fa_restore_idx = (
+            attn_metadata.prefill.pcp_metadata.pcp_enter_fa_restore_idx if attn_metadata.prefill.pcp_metadata else None
+        )
+        actual_qkv = torch.index_select(all_qkv, 0, pcp_enter_fa_restore_idx)
+        qkv_fa_padding_workspace = query.new_empty(
+            (num_actual_tokens_pcp_padded, (self.num_heads + 2 * self.num_kv_heads) * self.head_size)
+        )
+
+        decode_offset = attn_metadata.num_decode_tokens * self.pcp_size
+        qkv_fa_padding_workspace[:decode_offset] = actual_qkv[:decode_offset]
+
+        pcp_unpad_mask = attn_metadata.prefill.pcp_metadata.pcp_unpad_mask[attn_metadata.num_decodes * self.pcp_size :]
+        qkv_fa_padding_workspace[decode_offset:][pcp_unpad_mask] = actual_qkv[decode_offset:]
+
+        q, k, v = qkv_fa_padding_workspace.split(
+            [
+                self.num_heads * self.head_size,
+                self.num_kv_heads * self.head_size,
+                self.num_kv_heads * self.head_size,
+            ],
+            dim=-1,
+        )
+
+        return (
+            q.reshape(-1, self.num_heads, self.head_size),
+            k.reshape(-1, self.num_kv_heads, self.head_size),
+            v.reshape(-1, self.num_kv_heads, self.head_size),
+        )
 
     def _gather_global_context_output(self, local_context_attn_output):
         if self.dcp_size > 1:
@@ -831,8 +909,15 @@ class AscendAttentionCPImpl(AscendAttentionBackendImpl):
         has_decode = attn_metadata.num_decodes > 0
         has_prefill = attn_metadata.num_prefills > 0
         num_decode_tokens = attn_metadata.num_decode_tokens
+        pcp_use_hybrid_attn = False
+        if has_prefill:
+            assert attn_metadata.prefill is not None and attn_metadata.prefill.pcp_metadata is not None
+            pcp_use_hybrid_attn = attn_metadata.prefill.pcp_metadata.pcp_use_hybrid_attn
         if has_decode:
-            decode_query = query[:num_decode_tokens]
+            if pcp_use_hybrid_attn:
+                decode_query = query[: num_decode_tokens * self.pcp_size : self.pcp_size].contiguous()
+            else:
+                decode_query = query[:num_decode_tokens].contiguous()
             output_decode = self._forward_decode_pcp_dcp(decode_query, attn_metadata)
             output[:num_decode_tokens] = output_decode
         if has_prefill:
@@ -849,7 +934,10 @@ class AscendAttentionCPImpl(AscendAttentionBackendImpl):
 
             # qkv init
             num_actual_tokens_pcp_padded = attn_metadata.num_actual_tokens_pcp_padded // self.pcp_size
-            prefill_query = query[num_decode_tokens:num_actual_tokens_pcp_padded].contiguous()
+            if pcp_use_hybrid_attn:
+                prefill_query = query[self.pcp_size * num_decode_tokens :]
+            else:
+                prefill_query = query[num_decode_tokens:num_actual_tokens_pcp_padded].contiguous()
             key = key[self.pcp_size * num_decode_tokens :].contiguous()
             value = value[self.pcp_size * num_decode_tokens :].contiguous()
 
@@ -914,5 +1002,14 @@ class AscendAttentionCPImpl(AscendAttentionBackendImpl):
                     attn_output_prefill, attn_lse_prefill, context_output, context_lse, prefill_query, attn_metadata
                 )
 
+            if self.pcp_size > 1 and pcp_use_hybrid_attn:
+                # layer_idx != num_layers - 1
+                assert attn_metadata.prefill.pcp_metadata is not None
+                pcp_allgather_restore_idx = attn_metadata.prefill.pcp_metadata.pcp_allgather_restore_idx
+                attn_output_prefill = get_pcp_group().all_gather(attn_output_prefill.contiguous(), dim=0)
+                attn_output_prefill = torch.index_select(attn_output_prefill, 0, pcp_allgather_restore_idx)
+                fla_padding = attn_output_prefill.shape[0] + num_decode_tokens - output.shape[0]
+                output = F.pad(output, pad=(0, 0, 0, 0, 0, fla_padding), mode="constant", value=0)
+
             output[num_decode_tokens : attn_output_prefill.shape[0] + num_decode_tokens] = attn_output_prefill
         return output

vllm_ascend/attention/context_parallel/common_cp.py

@@ -25,7 +25,12 @@ class AscendPCPMetadata:
     head_attn_nomask_seqlens: torch.Tensor = None
     tail_attn_nomask_seqlens: torch.Tensor = None
     q_full_idx: torch.Tensor = None
+    pcp_use_hybrid_attn: bool = False
+    pcp_unpad_mask: torch.Tensor = None
     pcp_allgather_restore_idx: list[int] | None = None
+    pcp_fa_query_idx: torch.Tensor = None
+    pcp_padded_tokens_fla: int = 0
+    pcp_enter_fa_restore_idx: torch.Tensor = None
     block_table_cp: torch.Tensor = None
     valid_block_ids: torch.Tensor = None
     prefill_q_cum_seqlens: torch.Tensor = None

vllm_ascend/attention/utils.py

@@ -101,6 +101,21 @@ class AscendPrefillContextParallelMetadata:
     # original max_query_len before pcp split
     max_query_len_pcp_full: int = 0
 
+    # the following attributes are specifically used in hybrid-attn models.
+    pcp_use_hybrid_attn: bool = False
+
+    pcp_unpad_mask: torch.Tensor = None
+
+    # to get the right order of query in prefill per rank
+    pcp_fa_query_idx: torch.Tensor = None
+
+    # restore the full sequence across all pcp ranks
+    # when entering from linear-attention to attention
+    pcp_enter_fa_restore_idx: torch.Tensor = None
+
+    # the number of tokens padded in linear-attn per rank
+    pcp_padded_tokens_fla: int = 0
+
 
 @dataclass
 class AscendCommonAttentionMetadata(CommonAttentionMetadata):

vllm_ascend/ops/fused_moe/prepare_finalize.py

@@ -377,6 +377,15 @@ class PrepareAndFinalizeWithAllGather(PrepareAndFinalize):
             router_logits = self.moe_config.dp_group.all_gather(router_logits, 0)
 
         if prefill_context_parallel_enable() and self.moe_config.pcp_size > 1:
+            forward_context = get_forward_context()
+            max_tokens_across_pcp = forward_context.max_tokens_across_pcp
+
+            self.num_tokens_pcp = hidden_states.shape[0]
+            pad_size = max_tokens_across_pcp - self.num_tokens_pcp
+            if pad_size > 0:
+                hidden_states = nn.functional.pad(hidden_states, (0, 0, 0, pad_size))
+                router_logits = nn.functional.pad(router_logits, (0, 0, 0, pad_size))
+
             hidden_states = get_pcp_group().all_gather(
                 hidden_states,
                 dim=0,

vllm_ascend/ops/triton/fla/chunk.py

@@ -12,15 +12,19 @@ import warnings
 
 import torch
 from einops import rearrange
+from vllm.distributed import get_pcp_group
+from vllm.forward_context import get_forward_context
 from vllm.model_executor.layers.fla.ops.utils import SUPPRESS_LEVEL
 
 from .chunk_delta_h import chunk_gated_delta_rule_fwd_h
+from .chunk_delta_hupdate import chunk_gated_delta_rule_fwd_hupdate
 from .chunk_o import chunk_fwd_o
+from .chunk_o_update import chunk_fwd_o_update
 from .chunk_scaled_dot_kkt import chunk_scaled_dot_kkt_fwd
 from .cumsum import chunk_local_cumsum
 from .l2norm import l2norm_fwd
 from .solve_tril import solve_tril
-from .utils import input_guard
+from .utils import input_guard, prepare_final_chunk_indices
 from .wy_fast import recompute_w_u_fwd
 
 
@@ -35,7 +39,15 @@ def chunk_gated_delta_rule_fwd(
     output_final_state: bool,
     cu_seqlens: torch.LongTensor | None = None,
 ):
-    g = chunk_local_cumsum(g, chunk_size=64, cu_seqlens=cu_seqlens)
+    forward_context = get_forward_context()
+    num_decodes = 0
+    attn_metadata = forward_context.attn_metadata
+    if attn_metadata is not None and isinstance(attn_metadata, dict):
+        attn_metadata = next(iter(attn_metadata.values()), None)
+    if attn_metadata is not None:
+        num_decodes = attn_metadata.num_decodes
+    chunk_size = 64
+    g = chunk_local_cumsum(g, chunk_size=chunk_size, cu_seqlens=cu_seqlens)
     # obtain WY representation. u is actually the new v.
     A = chunk_scaled_dot_kkt_fwd(k=k, beta=beta, g_cumsum=g, cu_seqlens=cu_seqlens, output_dtype=torch.float32)
     A = solve_tril(A=A, cu_seqlens=cu_seqlens, output_dtype=k.dtype)
@@ -56,6 +68,45 @@ def chunk_gated_delta_rule_fwd(
         output_final_state=output_final_state,
         cu_seqlens=cu_seqlens,
     )
+
+    if get_pcp_group().world_size > 1:
+        h_update = chunk_gated_delta_rule_fwd_hupdate(
+            k=k,
+            w=w,
+            u=u,
+            g=g,
+            cu_seqlens=cu_seqlens,
+            num_decodes=num_decodes,
+        )
+        all_final_state = get_pcp_group().all_gather(final_state.unsqueeze(0), 0)
+        final_chunk_indices = prepare_final_chunk_indices(cu_seqlens, chunk_size)
+        final_h_update = h_update[:, final_chunk_indices, :, :, :]
+        all_final_h_update = get_pcp_group().all_gather(final_h_update, 0)
+
+        updated_state = final_state.new_empty(get_pcp_group().world_size, *final_state.shape)
+        updated_state[0, ...] = all_final_state[0]
+        for i in range(1, get_pcp_group().world_size):
+            updated_final_state = all_final_state[i] + torch.matmul(
+                all_final_h_update[i, ...], updated_state[i - 1, ...]
+            )
+            updated_state[i, ...] = updated_final_state
+
+        final_state = updated_state[-1, ...]
+
+        if get_pcp_group().rank_in_group == 0:
+            updated_h_state = torch.zeros_like(final_state)
+        else:
+            updated_h_state = updated_state[get_pcp_group().rank_in_group - 1, ...]
+
+        h = chunk_fwd_o_update(
+            q=q,
+            v=v_new,
+            h=h,
+            h_update=h_update,
+            updated_h_state=updated_h_state,
+            cu_seqlens=cu_seqlens,
+        )
+
     o = chunk_fwd_o(
         q=q,
         k=k,
@@ -65,6 +116,7 @@ def chunk_gated_delta_rule_fwd(
         scale=scale,
         cu_seqlens=cu_seqlens,
     )
+
     if SUPPRESS_LEVEL < 3:
         return g, o, A, final_state, None, None, None
     elif SUPPRESS_LEVEL >= 3:
@@ -90,7 +142,6 @@ class ChunkGatedDeltaRuleFunction(torch.autograd.Function):
         if use_qk_l2norm_in_kernel:
             q = l2norm_fwd(q)
             k = l2norm_fwd(k)
-
         g, o, A, final_state, w, h, v_new = chunk_gated_delta_rule_fwd(
             q=q,
             k=k,

vllm_ascend/ops/triton/fla/chunk_delta_h.py

@@ -38,6 +38,7 @@ def chunk_gated_delta_rule_fwd_kernel_h_blockdim64(
     ht,
     cu_seqlens,
     chunk_offsets,
+    h_update,
     T,
     H: tl.constexpr,
     Hg: tl.constexpr,
@@ -72,6 +73,7 @@ def chunk_gated_delta_rule_fwd_kernel_h_blockdim64(
 
     b_h1_bv1 = tl.zeros([128, 64], dtype=tl.float32)
     b_h1_bv2 = tl.zeros([128, 64], dtype=tl.float32)
+    # create b_hupd_bv1 and b_hupd_bv2
 
     v_start1 = 0
     v_start2 = 64
@@ -204,6 +206,7 @@ def chunk_gated_delta_rule_fwd_h(
     assert K <= 256, "current kernel does not support head dimension larger than 256."
 
     h = k.new_empty(B, NT, H, K, V)
+    h_update = k.new_empty(B, NT, H, K, K)
     final_state = k.new_empty(N, H, K, V, dtype=torch.float32) if output_final_state else None
 
     v_new = torch.empty_like(u) if save_new_value else None
@@ -223,6 +226,7 @@ def chunk_gated_delta_rule_fwd_h(
         ht=final_state,
         cu_seqlens=cu_seqlens,
         chunk_offsets=chunk_offsets,
+        h_update=h_update,
         T=T,
         H=H,
         Hg=Hg,

vllm_ascend/ops/triton/fla/chunk_delta_hupdate.py

@@ -0,0 +1,211 @@
+# SPDX-License-Identifier: Apache-2.0
+# SPDX-FileCopyrightText: Copyright contributors to the vLLM project
+# SPDX-FileCopyrightText: Songlin Yang, Yu Zhang
+#
+# This file contains code copied from the flash-linear-attention project.
+# The original source code was licensed under the MIT license and included
+# the following copyright notice:
+# Copyright (c) 2023-2025, Songlin Yang, Yu Zhang
+# ruff: noqa: E501
+# mypy: ignore-errors
+
+import torch
+from vllm.triton_utils import tl, triton
+
+from .utils import prepare_chunk_indices, prepare_chunk_offsets, prepare_update_chunk_offsets, safe_exp
+
+_CONDITIONS = ("seq7168",)
+
+
+@triton.heuristics(
+    {
+        "USE_G": lambda args: args["g"] is not None,
+        "IS_VARLEN": lambda args: args["cu_seqlens"] is not None,
+    }
+)
+@triton.jit(do_not_specialize=["T"])
+def chunk_gated_delta_rule_fwd_kernel_hupdate_blockdim64(
+    k,
+    w,
+    g,
+    cu_seqlens,
+    chunk_offsets,
+    h_update,
+    T,
+    H: tl.constexpr,
+    Hg: tl.constexpr,
+    K: tl.constexpr,
+    BT: tl.constexpr,
+    USE_G: tl.constexpr,
+    IS_VARLEN: tl.constexpr,
+):
+    i_nh = tl.program_id(1)
+    i_n, i_h = i_nh // H, i_nh % H
+    T_max = 1 * T
+    bos, eos = (
+        tl.load(cu_seqlens + i_n).to(tl.int32),
+        tl.load(cu_seqlens + i_n + 1).to(tl.int32),
+    )
+    T = eos - bos
+    NT = tl.cdiv(T, BT)
+    boh = tl.load(chunk_offsets + i_n).to(tl.int32)
+    if IS_VARLEN:
+        bos, eos = (
+            tl.load(cu_seqlens + i_n).to(tl.int32),
+            tl.load(cu_seqlens + i_n + 1).to(tl.int32),
+        )
+        T = eos - bos
+        NT = tl.cdiv(T, BT)
+        boh = tl.load(chunk_offsets + i_n).to(tl.int32)
+    else:
+        bos, eos = i_n * T, i_n * T + T
+        NT = tl.cdiv(T, BT)
+        boh = i_n * NT
+
+    stride_k = Hg * K
+    stride_w = H * K
+
+    # create b_hupd_bv1 and b_hupd_bv2
+    off_hupd_1_top = tl.arange(0, 64)[:, None]
+    off_hupd_2_top = tl.arange(0, 64)[None, :]
+
+    # main recurrence
+    for i_t in range(NT):
+        last_idx = min((i_t + 1) * BT, T) - 1
+        b_g_last = tl.load(g + bos + i_h * T_max + last_idx)
+
+        offs_t = i_t * BT + tl.arange(0, BT)
+        mask_t = offs_t < T
+        g_ptr = g + bos + i_h * T_max
+        b_g = tl.load(g_ptr + offs_t, mask=mask_t, other=0.0)
+
+        b_g = safe_exp(b_g_last - b_g)
+        b_g_last = tl.exp(b_g_last)
+
+        offs_t_wv = (i_t * BT + tl.arange(0, BT))[:, None]
+        w_base = w + bos * H * K + i_h * K
+        # get column-sliced w [BT, 64]
+        offs_w_upd1 = tl.arange(0, 64)[None, :]
+        mask_w_upd1 = (offs_t_wv < T) & (offs_w_upd1 < K)
+        ptr_w_upd1 = w_base + offs_t_wv * stride_w + offs_w_upd1 * 1
+        b_w_upd1 = tl.load(ptr_w_upd1, mask=mask_w_upd1, other=0.0).to(tl.float32)
+
+        offs_w_upd2 = 64 + tl.arange(0, 64)[None, :]
+        mask_w_upd2 = (offs_t_wv < T) & (offs_w_upd2 < K)
+        ptr_w_upd2 = w_base + offs_t_wv * stride_w + offs_w_upd2 * 1
+        b_w_upd2 = tl.load(ptr_w_upd2, mask=mask_w_upd2, other=0.0).to(tl.float32)
+
+        k_base = k + bos * Hg * K + (i_h // (H // Hg)) * K
+        # get row-sliced k [64, T]
+        p_k_upd1 = tl.make_block_ptr(k_base, (K, T), (1, stride_k), (0, i_t * BT), (64, BT), (0, 1))
+        b_k_upd1 = tl.load(p_k_upd1, boundary_check=(0, 1))
+        p_k_upd2 = tl.make_block_ptr(k_base, (K, T), (1, stride_k), (64, i_t * BT), (64, BT), (0, 1))
+        b_k_upd2 = tl.load(p_k_upd2, boundary_check=(0, 1))
+
+        if USE_G:
+            b_w_upd1 = b_w_upd1 * b_g[:, None]
+            b_w_upd2 = b_w_upd2 * b_g[:, None]
+
+        # compute [64, BT] @ [BT, 64]
+        b_hupd_local_11 = (off_hupd_1_top == off_hupd_2_top).to(tl.float32)
+        b_hupd_local_22 = (off_hupd_1_top == off_hupd_2_top).to(tl.float32)
+
+        # fp32
+        if USE_G:
+            b_hupd_local_11 = b_hupd_local_11 * b_g_last
+            b_hupd_local_22 = b_hupd_local_22 * b_g_last
+
+        b_hupd_local_11 -= tl.dot(b_k_upd1, b_w_upd1.to(b_k_upd1.dtype))
+        b_hupd_local_22 -= tl.dot(b_k_upd2, b_w_upd2.to(b_k_upd2.dtype))
+        b_hupd_local_12 = -tl.dot(b_k_upd1, b_w_upd2.to(b_k_upd1.dtype)).to(tl.float32)
+        b_hupd_local_21 = -tl.dot(b_k_upd2, b_w_upd1.to(b_k_upd2.dtype)).to(tl.float32)
+
+        hupd_base = h_update + (boh + i_t + i_n) * H * K * K + i_h * K * K
+        p_hupd_11 = tl.make_block_ptr(hupd_base, (K, K), (K, 1), (0, 0), (64, 64), (1, 0))
+        b_hupd_11 = tl.load(p_hupd_11, boundary_check=(1, 0))
+        p_hupd_21 = tl.make_block_ptr(hupd_base, (K, K), (K, 1), (64, 0), (64, 64), (1, 0))
+        b_hupd_21 = tl.load(p_hupd_21, boundary_check=(1, 0))
+        p_hupd_12 = tl.make_block_ptr(hupd_base, (K, K), (K, 1), (0, 64), (64, 64), (1, 0))
+        b_hupd_12 = tl.load(p_hupd_12, boundary_check=(1, 0))
+        p_hupd_22 = tl.make_block_ptr(hupd_base, (K, K), (K, 1), (64, 64), (64, 64), (1, 0))
+        b_hupd_22 = tl.load(p_hupd_22, boundary_check=(1, 0))
+
+        b_hupd11_new = tl.dot(b_hupd_local_11.to(b_hupd_11.dtype), b_hupd_11).to(tl.float32)
+        b_hupd11_new += tl.dot(b_hupd_local_12.to(b_hupd_21.dtype), b_hupd_21)
+
+        b_hupd21_new = tl.dot(b_hupd_local_21.to(b_hupd_11.dtype), b_hupd_11).to(tl.float32)
+        b_hupd21_new += tl.dot(b_hupd_local_22.to(b_hupd_21.dtype), b_hupd_21)
+
+        b_hupd12_new = tl.dot(b_hupd_local_11.to(b_hupd_12.dtype), b_hupd_12).to(tl.float32)
+        b_hupd12_new += tl.dot(b_hupd_local_12.to(b_hupd_22.dtype), b_hupd_22)
+
+        b_hupd22_new = tl.dot(b_hupd_local_21.to(b_hupd_12.dtype), b_hupd_12).to(tl.float32)
+        b_hupd22_new += tl.dot(b_hupd_local_22.to(b_hupd_22.dtype), b_hupd_22)
+
+        hupd_next = h_update + (boh + i_t + i_n + 1) * H * K * K + i_h * K * K
+        p_hupd_11 = tl.make_block_ptr(hupd_next, (K, K), (K, 1), (0, 0), (64, 64), (1, 0))
+        tl.store(p_hupd_11, b_hupd11_new.to(p_hupd_11.dtype.element_ty), boundary_check=(0, 1))
+
+        p_hupd_21 = tl.make_block_ptr(hupd_next, (K, K), (K, 1), (64, 0), (64, 64), (1, 0))
+        tl.store(p_hupd_21, b_hupd21_new.to(p_hupd_21.dtype.element_ty), boundary_check=(0, 1))
+
+        p_hupd_12 = tl.make_block_ptr(hupd_next, (K, K), (K, 1), (0, 64), (64, 64), (1, 0))
+        tl.store(p_hupd_12, b_hupd12_new.to(p_hupd_12.dtype.element_ty), boundary_check=(0, 1))
+
+        p_hupd_22 = tl.make_block_ptr(hupd_next, (K, K), (K, 1), (64, 64), (64, 64), (1, 0))
+        tl.store(p_hupd_22, b_hupd22_new.to(p_hupd_22.dtype.element_ty), boundary_check=(0, 1))
+
+
+def chunk_gated_delta_rule_fwd_hupdate(
+    k: torch.Tensor,
+    w: torch.Tensor,
+    u: torch.Tensor,
+    g: torch.Tensor | None = None,
+    chunk_size: int = 64,  # SY: remove this argument and force chunk size 64?
+    cu_seqlens: torch.LongTensor | None = None,
+    num_decodes: int = 0,
+) -> tuple[torch.Tensor, torch.Tensor, torch.Tensor, torch.Tensor]:
+    # This kernel is slightly different from fla to support Q/K with different head numbers.
+    # In fla, Q/K always have the same head number, so Hg is always equal to H.
+    B, T, Hg, K, _ = *k.shape, u.shape[-1]
+    H = u.shape[-2]
+    BT = chunk_size
+
+    chunk_indices = prepare_chunk_indices(cu_seqlens, chunk_size) if cu_seqlens is not None else None
+    # N: the actual number of sequences in the batch with either equal or variable lengths
+    if cu_seqlens is None:
+        N, NT, chunk_offsets = B, triton.cdiv(T, BT), None
+    else:
+        N, NT, chunk_offsets = (
+            len(cu_seqlens) - 1,
+            len(chunk_indices),
+            prepare_chunk_offsets(cu_seqlens, BT),
+        )
+    assert K <= 256, "current kernel does not support head dimension larger than 256."
+
+    h_update = k.new_empty(B, NT + N, H, K, K, dtype=torch.float32)
+    update_indices = prepare_update_chunk_offsets(cu_seqlens, BT)[:-1]
+    h_update[:, update_indices, :, :, :] = torch.eye(K, dtype=h_update.dtype, device=h_update.device)
+
+    g = g.transpose(1, 2).contiguous()
+
+    def grid(meta):
+        return (1, N * H)
+
+    chunk_gated_delta_rule_fwd_kernel_hupdate_blockdim64[grid](
+        k=k,
+        w=w,
+        g=g,
+        cu_seqlens=cu_seqlens,
+        chunk_offsets=chunk_offsets,
+        h_update=h_update,
+        T=T,
+        H=H,
+        Hg=Hg,
+        K=K,
+        BT=BT,
+        num_warps=4,
+        num_stages=2,
+    )
+    h_update[:, : num_decodes * 2, :, :, :] = torch.zeros((K, K), dtype=h_update.dtype, device=h_update.device)
+    return h_update

vllm_ascend/ops/triton/fla/chunk_o_update.py

@@ -0,0 +1,121 @@
+# SPDX-License-Identifier: Apache-2.0
+# SPDX-FileCopyrightText: Copyright contributors to the vLLM project
+# SPDX-FileCopyrightText: Songlin Yang, Yu Zhang
+#
+# This file contains code copied from the flash-linear-attention project.
+# The original source code was licensed under the MIT license and included
+# the following copyright notice:
+# Copyright (c) 2023-2025, Songlin Yang, Yu Zhang
+
+# ruff: noqa: E501
+# mypy: ignore-errors
+
+import torch
+from vllm.triton_utils import tl, triton
+
+from .utils import prepare_chunk_offsets
+
+
+@triton.heuristics(
+    {
+        "IS_VARLEN": lambda args: args["cu_seqlens"] is not None,
+    }
+)
+@triton.jit(do_not_specialize=["T"])
+def chunk_fwd_kernel_o_update(
+    h,
+    h_update,
+    updated_h_state,
+    cu_seqlens,
+    chunk_offsets,
+    T,
+    H: tl.constexpr,
+    Hg: tl.constexpr,
+    K: tl.constexpr,
+    V: tl.constexpr,
+    BT: tl.constexpr,
+    BK: tl.constexpr,
+    BV: tl.constexpr,
+    IS_VARLEN: tl.constexpr,
+):
+    i_v, i_nh = tl.program_id(0), tl.program_id(1)
+    i_n, i_h = i_nh // H, i_nh % H  # splitting by the head of the req
+
+    if IS_VARLEN:
+        bos, eos = tl.load(cu_seqlens + i_n).to(tl.int32), tl.load(cu_seqlens + i_n + 1).to(tl.int32)
+        T = eos - bos
+        NT = tl.cdiv(T, BT)
+        boh = tl.load(chunk_offsets + i_n).to(tl.int64)
+    else:
+        bos, eos = i_n * T, i_n * T + T
+        NT = tl.cdiv(T, BT)
+        boh = i_n * NT
+
+    # offset calculation
+    updated_h_state += (i_n * H + i_h).to(tl.int64) * K * V
+
+    for i_t in range(NT):
+        i_tg = boh + i_t
+        h_base = h + (i_tg * H + i_h).to(tl.int64) * K * V
+        hupd_base = h_update + ((i_tg + i_n) * H + i_h).to(tl.int64) * K * K
+
+        for i_k in range(tl.cdiv(K, BK)):
+            p_h = tl.make_block_ptr(h_base, (K, V), (V, 1), (i_k * BK, i_v * BV), (BK, BV), (1, 0))
+            p_hupd = tl.make_block_ptr(hupd_base, (K, V), (V, 1), (i_k * BK, i_v * BV), (BK, BK), (1, 0))
+            p_updated_h_state = tl.make_block_ptr(
+                updated_h_state, (K, V), (V, 1), (i_k * BK, i_v * BV), (BK, BV), (1, 0)
+            )
+
+            # [BK, BV]
+            b_h = tl.load(p_h, boundary_check=(0, 1))
+            # [BK, BK]
+            b_hupd = tl.load(p_hupd, boundary_check=(0, 1))
+            # [BK, BV]
+            b_updated_h_state = tl.load(p_updated_h_state, boundary_check=(0, 1))
+
+            b_h += tl.dot(b_hupd.to(tl.bfloat16), b_updated_h_state.to(tl.bfloat16))
+            tl.store(p_h, b_h.to(p_h.dtype.element_ty), boundary_check=(0, 1))
+
+
+def chunk_fwd_o_update(
+    q: torch.Tensor,
+    v: torch.Tensor,
+    h: torch.Tensor,
+    h_update: torch.Tensor,
+    updated_h_state: torch.Tensor,
+    cu_seqlens: torch.LongTensor | None = None,
+    chunk_size: int = 64,
+) -> torch.Tensor:
+    B, T, Hg, K, V = *q.shape, v.shape[-1]
+    H = v.shape[-2]
+    BT = chunk_size
+
+    if cu_seqlens is None:
+        N, chunk_offsets = B, None
+    else:
+        N, chunk_offsets = (
+            len(cu_seqlens) - 1,
+            prepare_chunk_offsets(cu_seqlens, BT),
+        )
+
+    def grid(meta):
+        return (triton.cdiv(V, meta["BV"]), N * H)
+
+    chunk_fwd_kernel_o_update[grid](
+        h=h,
+        h_update=h_update,
+        updated_h_state=updated_h_state,
+        cu_seqlens=cu_seqlens,
+        chunk_offsets=chunk_offsets,
+        T=T,
+        H=H,
+        Hg=Hg,
+        K=K,
+        V=V,
+        BT=BT,
+        BK=128,
+        BV=128,
+        num_warps=4,
+        num_stages=2,
+    )
+    return h

vllm_ascend/ops/triton/fla/utils.py

@@ -24,10 +24,19 @@ def prepare_chunk_indices(cu_seqlens: torch.LongTensor, chunk_size: int) -> torc
     return torch.stack([indices.eq(0).cumsum(0) - 1, indices], 1).to(cu_seqlens)
 
 
+def prepare_final_chunk_indices(cu_seqlens: torch.LongTensor, chunk_size: int) -> torch.LongTensor:
+    indices = triton.cdiv(prepare_lens(cu_seqlens), chunk_size) + 1
+    return torch.cumsum(indices, 0) - 1
+
+
 def prepare_chunk_offsets(cu_seqlens: torch.LongTensor, chunk_size: int) -> torch.LongTensor:
     return torch.cat([cu_seqlens.new_tensor([0]), triton.cdiv(prepare_lens(cu_seqlens), chunk_size)]).cumsum(-1)
 
 
+def prepare_update_chunk_offsets(cu_seqlens: torch.LongTensor, chunk_size: int) -> torch.LongTensor:
+    return torch.cat([cu_seqlens.new_tensor([0]), triton.cdiv(prepare_lens(cu_seqlens), chunk_size) + 1]).cumsum(-1)
+
+
 def input_guard(fn: Callable[..., torch.Tensor]) -> Callable[..., torch.Tensor]:
     """
     A decorator to make sure all input tensors are contiguous and set the device based on input tensors.

vllm_ascend/ops/triton/mamba/causal_conv1d.py

@@ -13,6 +13,8 @@ import torch
 import torch.nn.functional as F
 import triton
 import triton.language as tl
+from vllm.distributed import get_pcp_group
+from vllm.forward_context import get_forward_context
 from vllm.v1.attention.backends.utils import PAD_SLOT_ID  # type: ignore
 
 
@@ -96,6 +98,14 @@ def causal_conv1d_fn(
             indices 0 and 3
     out: (batch, dim, seqlen)
     """
+    forward_context = get_forward_context()
+    num_decodes = 0
+    attn_metadata = forward_context.attn_metadata
+    if attn_metadata is not None and isinstance(attn_metadata, dict):
+        attn_metadata = next(iter(attn_metadata.values()), None)
+    if attn_metadata is not None:
+        num_decodes = attn_metadata.num_decodes
+
     if activation not in [None, "silu", "swish"]:
         raise NotImplementedError("activation must be None, silu, or swish")
     if x.stride(-1) != 1:
@@ -108,6 +118,13 @@ def causal_conv1d_fn(
     seqlens = seqlens.tolist()
     splits = torch.split(x, seqlens, dim=-1)
     width = weight.shape[1]
+    last_width_prefill_x = extract_last_width(x, query_start_loc[num_decodes:], conv_states.shape[-1])
+
+    if get_pcp_group().world_size > 1:
+        all_last_width_prefill_x = get_pcp_group().all_gather(last_width_prefill_x.unsqueeze(0).contiguous(), 0)
+        pcp_rank = get_pcp_group().rank_in_group
+        if pcp_rank > 0:
+            conv_states[cache_indices[num_decodes:]] = all_last_width_prefill_x[pcp_rank - 1, ...]
 
     for i in range(len(seqlens)):
         x_s = splits[i]
@@ -121,14 +138,25 @@ def causal_conv1d_fn(
                 activation=activation,
                 return_final_states=True,
                 final_states_out=conv_states[cache_indices[i]][..., : (width - 1)].unsqueeze(0),
-                initial_states=conv_states[cache_indices[i]][..., : (width - 1)] if has_initial_state[i] else None,
+                initial_states=conv_states[cache_indices[i]][..., : (width - 1)],
             )
         )
+
+    if get_pcp_group().world_size > 1:
+        conv_states[cache_indices[num_decodes:]] = all_last_width_prefill_x[-1, ...]
     out_ref.append(torch.cat([t[0] for t in out_ref_b], dim=-1))
     out_ref_tensor = torch.cat(out_ref, dim=0)
     return out_ref_tensor
 
 
+def extract_last_width(x, start_loc, width):
+    end_loc = start_loc[1:]
+    offsets = torch.arange(width, device=x.device)
+    indices = end_loc.unsqueeze(1) - width + offsets.unsqueeze(0)  # (num_seqs, width)
+
+    return x[:, indices].permute(1, 0, 2)
+
+
 @triton.jit
 def _causal_conv1d_update_kernel_npu_tiled(
     # Pointers

vllm_ascend/worker/model_runner_v1.py

@@ -383,6 +383,7 @@ class NPUModelRunner(GPUModelRunner):
         self.intermediate_tensors: IntermediateTensors | None = None
         self.reorder_batch_threshold: int | None = None
         self.long_seq_metadata = None
+        self.query_lens: torch.Tensor | None = None
         self.cpu_slot_mapping = None
 
     @property
@@ -543,10 +544,12 @@ class NPUModelRunner(GPUModelRunner):
         self,
         scheduler_output: "SchedulerOutput",
         num_scheduled_tokens: np.ndarray,
-    ) -> tuple[torch.Tensor, SpecDecodeMetadata | None]:
+    ) -> tuple[torch.Tensor, SpecDecodeMetadata | None, int]:
         """
         :return: tuple[
-            logits_indices, spec_decode_metadata,
+            logits_indices,
+            spec_decode_metadata,
+            total_num_scheduled_tokens,
         ]
         """
         total_num_scheduled_tokens = scheduler_output.total_num_scheduled_tokens
@@ -610,11 +613,10 @@ class NPUModelRunner(GPUModelRunner):
 
         if self.pcp_size > 1:
             num_scheduled_tokens[:num_reqs], position_pcp = self.pcp_manager.update_tokens_for_pcp(
-                num_scheduled_tokens[:num_reqs],
-                self.arange_np,
+                num_scheduled_tokens[:num_reqs], self.arange_np
             )
             # Re-update after PCP split sequences.
-            total_num_scheduled_tokens = sum(num_scheduled_tokens)
+            total_num_scheduled_tokens = sum(num_scheduled_tokens[:num_reqs])
             req_indices = np.repeat(self.arange_np[:num_reqs], num_scheduled_tokens)
             cu_num_tokens, _ = self._get_cumsum_and_arange(num_scheduled_tokens)
             positions_np = self.positions.np[:total_num_scheduled_tokens]
@@ -623,7 +625,11 @@ class NPUModelRunner(GPUModelRunner):
                 position_pcp[:total_num_scheduled_tokens],
                 out=positions_np,
             )
-        self.query_lens = torch.from_numpy(num_scheduled_tokens)
+        if self.pcp_size > 1 and self.pcp_manager.pcp_use_hybrid_attn:
+            assert self.pcp_manager.num_scheduled_tokens_padded is not None
+            self.query_lens = torch.from_numpy(self.pcp_manager.num_scheduled_tokens_padded)
+        else:
+            self.query_lens = torch.from_numpy(num_scheduled_tokens)
 
         # Get token indices.
         # E.g., [0, 1, 0, 1, 2, 3, 4, 0, 1, 2]
@@ -702,6 +708,8 @@ class NPUModelRunner(GPUModelRunner):
         self.seq_lens.np[:num_reqs] = self.input_batch.num_computed_tokens_cpu[:num_reqs] + num_scheduled_tokens
         self.seq_lens.copy_to_gpu()
 
+        # Fill unused with -1. Needed for reshape_and_cache in attention_cp
+        self.query_start_loc.gpu[num_reqs + 1 :].fill_(-1)
         self.seq_lens.gpu[num_reqs:].fill_(0)
 
         # Copy the tensors to the NPU.
@@ -732,6 +740,7 @@ class NPUModelRunner(GPUModelRunner):
         num_tokens_np = np.array(num_tokens, dtype=np.int32)
         base_num_reqs = self.input_batch.num_reqs
         num_reqs = base_num_reqs
+        tokens_original = None
         if self.pcp_size > 1:
             # while pcp > 1, we need the original num_scheduled_tokens before split
             # to calculate discard_requests_mask
@@ -758,7 +767,7 @@ class NPUModelRunner(GPUModelRunner):
             num_draft_tokens = None
             num_sampled_tokens = np.ones(num_reqs, dtype=np.int32)
             if self.use_cp:
-                logits_indices = self.pcp_manager.get_logits_indices(cu_num_tokens)
+                logits_indices = self.pcp_manager.get_logits_indices(cu_num_tokens, num_reqs, tokens_original)
                 logits_indices = logits_indices.pin_memory().to(self.device, non_blocking=True)
             else:
                 logits_indices = self.query_start_loc.gpu[1 : num_reqs + 1] - 1
@@ -807,7 +816,11 @@ class NPUModelRunner(GPUModelRunner):
             max_num_reqs_across_dp = self.max_num_reqs * self.uniform_decode_query_len
             logits_indices = nn.functional.pad(logits_indices, (0, max_num_reqs_across_dp - logits_indices.shape[0]))
 
-        return logits_indices, spec_decode_metadata
+        return (
+            logits_indices,
+            spec_decode_metadata,
+            total_num_scheduled_tokens,
+        )
 
     def _build_attn_state(self, num_reqs, num_scheduled_tokens, num_valid_tokens):
         if np.all(self.input_batch.num_computed_tokens_cpu[:num_reqs] == 0):
@@ -1152,6 +1165,7 @@ class NPUModelRunner(GPUModelRunner):
                 (
                     logits_indices,
                     spec_decode_metadata,
+                    total_num_scheduled_tokens,
                 ) = self._prepare_inputs(
                     scheduler_output,
                     num_scheduled_tokens_np,
@@ -1220,7 +1234,9 @@ class NPUModelRunner(GPUModelRunner):
                     num_reqs_padded = self._pad_query_start_loc_for_fia(num_tokens_padded, num_reqs_padded, num_reqs)
 
                 (attn_metadata, spec_decode_common_attn_metadata) = self._build_attention_metadata(
-                    num_tokens=num_tokens_unpadded,
+                    num_tokens=num_tokens_unpadded
+                    if not (self.use_cp and self.pcp_manager.pcp_use_hybrid_attn)
+                    else total_num_scheduled_tokens,
                     num_tokens_padded=num_tokens_padded,
                     num_reqs=num_reqs,
                     num_reqs_padded=num_reqs_padded,
@@ -1240,7 +1256,13 @@ class NPUModelRunner(GPUModelRunner):
                 intermediate_tensors,
                 model_kwargs,
                 ec_connector_output,
-            ) = self._preprocess(scheduler_output, num_tokens_padded, intermediate_tensors)
+            ) = self._preprocess(
+                scheduler_output,
+                num_tokens_padded
+                if not (self.use_cp and self.pcp_manager.pcp_use_hybrid_attn)
+                else total_num_scheduled_tokens,
+                intermediate_tensors,
+            )
 
             if self.dynamic_eplb:
                 self.eplb_updator.take_update_info_from_eplb_process()
@@ -1287,6 +1309,7 @@ class NPUModelRunner(GPUModelRunner):
                 batch_descriptor=batch_desc,
                 num_actual_tokens=scheduler_output.total_num_scheduled_tokens,
                 model_instance=self.model,
+                max_tokens_across_pcp=0 if self.pcp_size == 1 else self.pcp_manager.max_num_tokens_across_pcp,
                 skip_compiled=has_encoder_input,
             ),
             self.maybe_get_kv_connector_output(scheduler_output) as kv_connector_output,
@@ -1922,11 +1945,16 @@ class NPUModelRunner(GPUModelRunner):
         def _get_block_table_and_slot_mapping(kv_cache_gid: int):
             assert num_reqs_padded is not None and num_tokens_padded is not None
             kv_cache_spec = kv_cache_groups[kv_cache_gid].kv_cache_spec
-            maybe_pcp_full_tokens = (
-                num_tokens_padded
-                if self.pcp_size == 1
-                else num_tokens * self.pcp_size - sum(self.pcp_manager.num_pcp_pads_cpu[:num_reqs])
-            )
+            if self.pcp_size > 1:
+                total_num_pcp_pads = sum(self.pcp_manager.num_pcp_pads_cpu[:num_reqs])
+                if self.pcp_manager.pcp_use_hybrid_attn:
+                    num_scheduled_tokens_padded = self.pcp_manager.num_scheduled_tokens_padded
+                    assert num_scheduled_tokens_padded is not None
+                    maybe_pcp_full_tokens = sum(num_scheduled_tokens_padded) * self.pcp_size - total_num_pcp_pads
+                else:
+                    maybe_pcp_full_tokens = num_tokens * self.pcp_size - total_num_pcp_pads
+            else:
+                maybe_pcp_full_tokens = num_tokens_padded
             if isinstance(kv_cache_spec, EncoderOnlyAttentionSpec):
                 blk_table_tensor = torch.zeros(
                     (num_reqs_padded, 1),

vllm_ascend/worker/pcp_utils.py

@@ -21,6 +21,7 @@ from typing import TYPE_CHECKING
 
 import numpy as np
 import torch
+import torch.nn.functional as F
 from vllm.config import VllmConfig
 from vllm.v1.utils import CpuGpuBuffer
 
@@ -110,6 +111,20 @@ class PCPManager:
             self.query_lens_pcp_full = CpuGpuBuffer(
                 self.max_num_reqs, dtype=torch.int32, device=device, pin_memory=pin_memory
             )
+        self.pcp_fa_query_idx = torch.zeros(
+            self.max_num_tokens + 2 * self.max_num_reqs, dtype=torch.int32, device=self.device
+        )
+        self.pcp_enter_fa_restore_idx = torch.zeros(
+            self.max_num_tokens + 2 * self.pcp_world_size * self.max_num_reqs, dtype=torch.int32, device=self.device
+        )
+        self.pcp_use_hybrid_attn = self.vllm_config.model_config.hf_config.model_type == "qwen3_next"
+
+        self.pcp_pads_logits_hybrid_attn = torch.ones(self.max_num_reqs, dtype=torch.int32) * (self.pcp_world_size - 1)
+        self.pcp_padded_tokens_fla = 0
+        self.pcp_padded_tokens_length = 0
+        self.num_scheduled_tokens_padded = None
+        self.max_num_tokens_across_pcp = 0
+        self.pcp_tokens_padded = None
 
     def _get_cumsum_and_arange(
         self,
@@ -184,9 +199,10 @@ class PCPManager:
             Tuple (pcp_tokens, pcp_positions):
             - pcp_tokens: number of tokens per request that this PCP rank will
                           actually process (after splitting / replication).
+                          For hybrid-attention model: number of unpadded tokens
+                          per requests
             - pcp_positions: flattened positions for those tokens on this rank,
                              used to build the positions buffer for the model.
-
         Example:
         >>> Assume tokens = [1, 5, 8], pcp_world_size = 2. After _update_tokens_for_pcp.
         >>> pcp_rank = 0 get ([1, 4, 4], [0, 0, 1, 6, 7, 0, 1, 6, 7])
@@ -219,9 +235,10 @@ class PCPManager:
         # cu_padded_tokens: cumulative sum of padded token counts,
         # pcp_padded_arange: per-request arange flattened for padded tokens.
         cu_padded_tokens, pcp_padded_arange = self._get_cumsum_and_arange(num_padded_scheduled_tokens, arange_np)
+        self.pcp_padded_tokens_length = pcp_padded_arange.shape[0]
         # Build the mask that marks which positions in the padded allgather buffer
         # correspond to real (unpadded) tokens.
-        self.pcp_unpad_mask_cpu[: pcp_padded_arange.shape[0]] = pcp_padded_arange < np.repeat(
+        self.pcp_unpad_mask_cpu[: self.pcp_padded_tokens_length] = pcp_padded_arange < np.repeat(
             num_scheduled_tokens, num_padded_scheduled_tokens
         )
         unpad_mask_decode = self.pcp_unpad_mask_cpu[: self.num_decode_tokens * self.pcp_world_size]
@@ -272,6 +289,9 @@ class PCPManager:
             return positions
 
         positions = get_current_rank_positions(0, self.pcp_world_rank)
+        padded_pos_start_loc = np.roll(cu_padded_tokens, 1)
+        padded_pos_start_loc[0] = 0
+
         # Decode tokens are duplicated only after AG. But their positions are
         # same without prefill context parallel.
         if self.num_decode_reqs > 0:
@@ -279,35 +299,192 @@ class PCPManager:
                 num_scheduled_tokens[: self.num_decode_reqs], arange_np
             )[1]
 
-        # Build the restore index used after allgather.
-        padded_pos_start_loc = np.roll(cu_padded_tokens, 1)
-        padded_pos_start_loc[0] = 0
-        all_positions_lst = [
-            get_current_rank_positions(padded_pos_start_loc, rank_i) for rank_i in range(self.pcp_world_size)
-        ]
-        all_positions = np.concatenate(all_positions_lst)
-        self.pcp_allgather_restore_idx.np[: all_positions.shape[0]] = all_positions.argsort()
-        self.pcp_allgather_restore_idx.copy_to_gpu(all_positions.shape[0])
+        if self.pcp_use_hybrid_attn:
+            max_scheduled_prefill_tokens = 0
+            self.pcp_padded_tokens_fla = 0
+            if self.num_decode_reqs > 0:
+                num_padded_scheduled_tokens[: self.num_decode_reqs] = (
+                    num_padded_scheduled_tokens[: self.num_decode_reqs] // self.pcp_world_size
+                )
+            self.total_pcp_padding_tokens_fla = 0
+            # have prefills
+            if self.num_reqs - self.num_decode_reqs > 0:
+                prefill_tokens_tensor = torch.Tensor(num_scheduled_tokens[self.num_decode_tokens :])
+                # [num_prefill_reqs, pcp_world_size, 1] [[3,2]] [[2,2,2,1],[2,1,1,1]]
+                num_prefill_tokens_allranks = (
+                    self._get_cp_local_seq_lens(prefill_tokens_tensor, self.pcp_world_size, 1, 1).long().numpy()
+                )
+                # [3] [2]  |  [2,2] [2,1] [2,1] [1,1]
+                num_prefill_scheduled_tokens_linear = num_prefill_tokens_allranks[:, self.pcp_world_rank, 0]
+                num_padded_scheduled_tokens[self.num_decode_reqs :] = num_prefill_scheduled_tokens_linear
+                # [[3,5]] | [[0,0,0,0,0],[0,0,0,0,0]]
+                num_prefill_tokens_start_loc = np.zeros(
+                    (self.num_reqs - self.num_decode_reqs, self.pcp_world_size + 1), dtype=np.int64
+                )
+                # [[0,3,5]] | [[0,2,4,6,7],[0,2,3,4,5]]
+                num_prefill_tokens_start_loc[:, 1:] = np.cumsum(num_prefill_tokens_allranks[..., 0], axis=-1)
+                # [0] [3] | [0,0] [2,2] [4,3] [6,4] [7,5]
+                num_prefill_tokens_cu_ranks = num_prefill_tokens_start_loc[:, self.pcp_world_rank]
+                # [0,1,2] [0,1] | [0,1,0,1] [0,1,0] [0,1,0] [0,0]
+                # -> [0,1,2] [3,4] | [0,1,0,1] [2,3,2] [4,5,3] [6,4]
+                _, positions_linear = self._get_cumsum_and_arange(num_padded_scheduled_tokens, arange_np)
+                positions_linear[self.num_decode_reqs :] = positions_linear[self.num_decode_reqs :] + np.repeat(
+                    num_prefill_tokens_cu_ranks, num_prefill_scheduled_tokens_linear
+                )
 
-        self.pcp_tokens[: self.num_reqs] = pcp_tokens[: self.num_reqs]
-        self.total_num_sampled_tokens_pcp = pcp_tokens[: self.num_reqs].sum()
-        return (
-            pcp_tokens[: self.num_reqs],
-            positions,
-        )
+                max_scheduled_prefill_tokens = num_prefill_tokens_allranks[:, 0, 0].sum()
+                num_prefill_tokens = num_scheduled_tokens[self.num_decode_reqs :].sum()
+                self.total_pcp_padding_tokens_fla = (
+                    max_scheduled_prefill_tokens * self.pcp_world_size - num_prefill_tokens
+                )
+                self.pcp_padded_tokens_fla += max_scheduled_prefill_tokens - num_prefill_scheduled_tokens_linear.sum()
 
-    def get_logits_indices(self, cu_num_tokens: np.ndarray):
-        return torch.from_numpy(cu_num_tokens) * self.pcp_world_size - self.num_pcp_pads_cpu_tensor[: self.num_reqs] - 1
+            max_scheduled_tokens = max_scheduled_prefill_tokens + self.num_decode_tokens
+            enter_fa_prefill_restore_idx = None
+            if self.num_reqs - self.num_decode_reqs > 0:
+                # prefill reorder idx
+                # [[3,2]] [[2,2,2,1],[2,2,1,1],[1,1,1,1]]
+                num_prefill_tokens_allranks = num_prefill_tokens_allranks[..., 0]
+                # [0,1,2,0,1] [0,1,0,1,0,1,0,|0,1,0,1,0,0]
+                _, prefill_arange_allranks = self._get_cumsum_and_arange(
+                    num_prefill_tokens_allranks.flatten(), arange_np
+                )
+                # [0,1] [0,1,2,3,0,1,2,3]
+                _, prefill_rank_offset = self._get_cumsum_and_arange(
+                    np.ones(self.num_reqs - self.num_decode_reqs, dtype=np.int64) * self.pcp_world_size, arange_np
+                )
+                # [0,0,0,3,3] [0,M,2M,3M,0,M,2M,3M] -> [0,0,M,M,2M,2M,3M,0,0,M,M,2M,3M] + D
+                prefill_all_offset = (
+                    np.repeat(prefill_rank_offset * max_scheduled_tokens, num_prefill_tokens_allranks.flatten())
+                    + self.num_decode_tokens
+                )
+
+                # [0,0,0,0,|2,2,2,1,|4,4,3,2] -> [0,0,0,0,0,0,0,|2,2,2,2,2,1,|4,4,3,2]
+                # [[0,0]] -> [0,0,0,0,0]
+                prefill_local_start_local = np.zeros_like(num_prefill_tokens_allranks)
+                prefill_local_start_local[1:, :] = np.cumsum(num_prefill_tokens_allranks, axis=0)[:-1, :]
+                prefill_local_offset = np.repeat(
+                    prefill_local_start_local.flatten(), num_prefill_tokens_allranks.flatten()
+                )
+                prefill_all_offset = np.add(prefill_all_offset, prefill_local_offset)
+                # [0,1,2,3,4]  [0,1,M,M+1,2M,2M+1,3M,0,1,M,M+1,2M,3M]
+                enter_fa_prefill_restore_idx = np.add(prefill_all_offset, prefill_arange_allranks)
+            else:
+                _, positions_linear = self._get_cumsum_and_arange(num_padded_scheduled_tokens, arange_np)
+
+            # decode reorder idx
+            enter_fa_decode_restore_idx = None
+            if self.num_decode_reqs > 0:
+                # [0,1,2], [4,4,4] -> [0,0,0,0,1,1,1,1,2,2,2,2]
+                num_decode_pcp_size = np.ones(self.num_decode_reqs, dtype=np.int64) * self.pcp_world_size
+                decode_reqs_offset = np.repeat(np.arange(self.num_decode_reqs, dtype=np.int64), num_decode_pcp_size)
+                decode_ranks_offset = (
+                    self._get_cumsum_and_arange(num_decode_pcp_size, arange_np)[1] * max_scheduled_tokens
+                )
+                enter_fa_decode_restore_idx = np.add(decode_reqs_offset, decode_ranks_offset)
+
+            if enter_fa_decode_restore_idx is not None and enter_fa_prefill_restore_idx is not None:
+                pcp_enter_fa_restore_idx = torch.from_numpy(
+                    np.concatenate([enter_fa_decode_restore_idx, enter_fa_prefill_restore_idx])
+                )
+            elif enter_fa_decode_restore_idx is not None:
+                pcp_enter_fa_restore_idx = torch.from_numpy(enter_fa_decode_restore_idx)
+
+            elif enter_fa_prefill_restore_idx is not None:
+                pcp_enter_fa_restore_idx = torch.from_numpy(enter_fa_prefill_restore_idx)
+            self.pcp_enter_fa_restore_idx[: pcp_enter_fa_restore_idx.shape[0]].copy_(
+                pcp_enter_fa_restore_idx.long(), non_blocking=True
+            )
+
+            if self.num_reqs > self.num_decode_reqs:
+                all_positions_prefill = [
+                    get_current_rank_positions(padded_pos_start_loc, rank_i)[self.num_decode_tokens :]
+                    - self.num_decode_tokens * self.pcp_world_size
+                    for rank_i in range(self.pcp_world_size)
+                ]
+                all_positions_prefill_tensor = torch.from_numpy(np.concatenate(all_positions_prefill))
+                all_enter_fla_restore_idx = all_positions_prefill_tensor.float().argsort()
+                unpad_mask_prefill = self.pcp_unpad_mask_cpu[: self.pcp_padded_tokens_length][
+                    self.num_decode_reqs * self.pcp_world_size :
+                ]
+                # [0] | [0,7]
+                ori_tokens_start_loc = np.roll(np.cumsum(num_scheduled_tokens[self.num_decode_tokens :]), 1)
+                ori_tokens_start_loc[0] = 0
+                # [0,1,2] [3,4] | [0,1,7,8] [2,3,9] [4,5,10] [6,11]
+                enter_fla_scatter_idx = positions_linear[self.num_decode_reqs :] + np.repeat(
+                    ori_tokens_start_loc, num_prefill_scheduled_tokens_linear
+                )
+                enter_fla_restore_idx = torch.index_select(
+                    all_enter_fla_restore_idx[unpad_mask_prefill], 0, torch.from_numpy(enter_fla_scatter_idx)
+                )
+                self.pcp_allgather_restore_idx.gpu[: enter_fla_restore_idx.shape[0]].copy_(
+                    enter_fla_restore_idx.long(), non_blocking=True
+                )
+
+                positions_prefill = all_positions_prefill[self.pcp_world_rank]
+                pcp_fa_query_idx_tensor = torch.from_numpy(positions_prefill)
+                self.pcp_fa_query_idx[: pcp_fa_query_idx_tensor.shape[0]].copy_(
+                    pcp_fa_query_idx_tensor.long(), non_blocking=True
+                )
+            self.pcp_tokens[: self.num_reqs] = pcp_tokens[: self.num_reqs]
+            self.total_num_sampled_tokens_pcp = num_scheduled_tokens[: self.num_reqs].sum()
+            self.max_num_tokens_across_pcp = max_scheduled_tokens
+            self.pcp_tokens_padded = pcp_tokens[: self.num_reqs]
+            self.num_scheduled_tokens_padded = np.array(self.pcp_tokens_padded, dtype=np.int32)
+            return num_padded_scheduled_tokens, positions_linear
+        else:
+            # Build the restore index used after allgather.
+            all_positions_lst = [
+                get_current_rank_positions(padded_pos_start_loc, rank_i) for rank_i in range(self.pcp_world_size)
+            ]
+            all_positions = np.concatenate(all_positions_lst)
+            self.pcp_allgather_restore_idx.np[: all_positions.shape[0]] = all_positions.argsort()
+            self.pcp_allgather_restore_idx.copy_to_gpu(all_positions.shape[0])
+
+            self.pcp_tokens[: self.num_reqs] = pcp_tokens[: self.num_reqs]
+            self.total_num_sampled_tokens_pcp = pcp_tokens[: self.num_reqs].sum()
+            return pcp_tokens[: self.num_reqs], positions
+
+    def get_logits_indices(
+        self,
+        cu_num_tokens: np.ndarray,
+        num_reqs: int,
+        tokens_original: list[int] | None = None,
+    ):
+        if not self.pcp_use_hybrid_attn or tokens_original is None:
+            logits_indices = (
+                torch.from_numpy(cu_num_tokens) * self.pcp_world_size
+                - self.num_pcp_pads_cpu_tensor[: self.num_reqs]
+                - 1
+            )
+        else:
+            tokens_original_tensor = torch.tensor(tokens_original, dtype=torch.int32)
+            num_prefill_reqs = (tokens_original_tensor > self.decode_threshold).sum().item()
+            num_decode_reqs = num_reqs - num_prefill_reqs
+            decode_pads = self.pcp_pads_logits_hybrid_attn[:num_decode_reqs]
+            pad_len = tokens_original_tensor.shape[0] - num_decode_reqs
+            tokens_logits = tokens_original_tensor + F.pad(decode_pads, (0, pad_len), value=0)
+            logits_indices = torch.cumsum(tokens_logits, dim=0) - 1
+        return logits_indices
 
     def get_padded_slot_mapping(self, num_tokens: int, num_tokens_padded: int, slot_mapping: torch.Tensor):
         # After pcp allgather and restore, there are padded tokens in kv,
         # so we need pad slotmapping for alignment.
-        pcp_padded_slot_mapping = self.pcp_padded_slot_mapping[: num_tokens_padded * self.pcp_world_size]
-
+        if self.pcp_use_hybrid_attn:
+            assert self.num_scheduled_tokens_padded is not None
+            num_tokens = self.num_scheduled_tokens_padded.sum()
+        pcp_padded_slot_mapping = (
+            self.pcp_padded_slot_mapping[: num_tokens_padded * self.pcp_world_size]
+            if not self.pcp_use_hybrid_attn
+            else self.pcp_padded_slot_mapping[: num_tokens * self.pcp_world_size]
+        )
         cp_unpad_mask = self.pcp_unpad_mask_cpu_tensor[: num_tokens * self.pcp_world_size]
         pcp_padded_slot_mapping.fill_(-1)
         pcp_padded_slot_mapping[: num_tokens * self.pcp_world_size][cp_unpad_mask] = slot_mapping
-        return pcp_padded_slot_mapping
+        if self.pcp_use_hybrid_attn:
+            return pcp_padded_slot_mapping.clone()
+        else:
+            return pcp_padded_slot_mapping
 
     def get_restore_hidden_states(
         self,
@@ -317,16 +494,25 @@ class PCPManager:
         # ignores the padding from CUDA Graph.
         from vllm.distributed.parallel_state import get_pcp_group
 
-        hidden_states = get_pcp_group().all_gather(
-            hidden_states[: self.num_actual_tokens_pcp_padded // self.pcp_world_size],
-            0,
-        )
-        restore_idx = self.pcp_allgather_restore_idx.gpu[: hidden_states.shape[0]]
-        return torch.index_select(
-            hidden_states,
-            0,
-            restore_idx,
-        )
+        if not self.pcp_use_hybrid_attn:
+            hidden_states = get_pcp_group().all_gather(
+                hidden_states[: self.num_actual_tokens_pcp_padded // self.pcp_world_size],
+                0,
+            )
+            restore_idx = self.pcp_allgather_restore_idx.gpu[: hidden_states.shape[0]]
+            return torch.index_select(
+                hidden_states,
+                0,
+                restore_idx,
+            )
+        else:
+            if self.pcp_padded_tokens_fla > 0:
+                hidden_states = F.pad(
+                    hidden_states, pad=(0, 0, 0, self.pcp_padded_tokens_fla), mode="constant", value=0
+                )
+            hidden_states = get_pcp_group().all_gather(hidden_states.contiguous(), dim=0)
+            restore_idx = self.pcp_enter_fa_restore_idx[: hidden_states.shape[0] - self.total_pcp_padding_tokens_fla]
+            return torch.index_select(hidden_states, 0, restore_idx)
 
     def generate_pcp_mtp_input(
         self,
@@ -528,6 +714,15 @@ class PCPManager:
     ):
         from vllm_ascend.attention.utils import AscendPrefillContextParallelMetadata
 
+        if self.pcp_world_size > 1 and self.pcp_use_hybrid_attn:
+            assert self.num_scheduled_tokens_padded is not None
+            total_num_scheduled_tokens = self.num_scheduled_tokens_padded.sum()
+        query_lens_new = (
+            self.query_lens_pcp_full.cpu[:num_reqs]
+            if self.pcp_world_size > 1 and self.speculative_config
+            else query_lens
+        )
+        num_decodes = (query_lens_new <= self.decode_threshold).sum().item()
         num_actual_tokens_pcp_padded = total_num_scheduled_tokens * self.pcp_world_size
         self.num_actual_tokens_pcp_padded = num_actual_tokens_pcp_padded
         long_seq_metadata = None
@@ -599,10 +794,13 @@ class PCPManager:
                 if num_reqs_padded > num_reqs:
                     pad_size = num_reqs_padded - num_reqs
                     ori_query_lens_cpu[-pad_size:] = torch.full([pad_size], ori_query_lens_cpu[-pad_size - 1].item())
-
+            pcp_unpad_mask = self.pcp_unpad_mask_cpu[: self.pcp_padded_tokens_length]
             long_seq_metadata = AscendPrefillContextParallelMetadata(
+                pcp_use_hybrid_attn=self.pcp_use_hybrid_attn,
                 num_actual_tokens_pcp_padded=num_actual_tokens_pcp_padded,
                 num_computed_tokens_of_pcp_dcp=num_computed_tokens_of_pcp_dcp.numpy(),
+                pcp_unpad_mask=torch.from_numpy(pcp_unpad_mask),
+                pcp_padded_tokens_fla=self.pcp_padded_tokens_fla,
             )
             if ori_query_lens_cpu is not None:
                 long_seq_metadata.query_lens_pcp_full_cpu = ori_query_lens_cpu
@@ -703,9 +901,20 @@ class PCPManager:
                     "head_attn_nomask_seqlens": head_attn_nomask_seqlens,
                     "tail_attn_nomask_seqlens": tail_attn_nomask_seqlens,
                 }
-                long_seq_metadata.pcp_allgather_restore_idx = self.pcp_allgather_restore_idx.gpu[
-                    :num_actual_tokens_pcp_padded
-                ]
+                if not self.pcp_use_hybrid_attn:
+                    long_seq_metadata.pcp_allgather_restore_idx = self.pcp_allgather_restore_idx.gpu[
+                        :num_actual_tokens_pcp_padded
+                    ]
+                else:
+                    long_seq_metadata.pcp_allgather_restore_idx = self.pcp_allgather_restore_idx.gpu[
+                        : num_scheduled_tokens.sum() - num_decodes
+                    ]
+                    long_seq_metadata.pcp_fa_query_idx = self.pcp_fa_query_idx[
+                        : num_actual_tokens_pcp_padded // self.pcp_world_size - num_decodes
+                    ]
+                    long_seq_metadata.pcp_enter_fa_restore_idx = self.pcp_enter_fa_restore_idx[
+                        : pcp_unpad_mask.sum() + num_decodes * (self.pcp_world_size - 1)
+                    ]
                 long_seq_metadata.q_head_idx_tensor = self.q_head_idx_tensor
                 long_seq_metadata.q_tail_idx_tensor = self.q_tail_idx_tensor
                 long_seq_metadata.q_full_idx = self.q_full_idx